A library for scientific machine learning and physics-informed learning
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Updated
Jun 9, 2024 - Python
A library for scientific machine learning and physics-informed learning
Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
Pre-built implicit layer architectures with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learning (SciML) and physics-informed machine learning methods
A library for solving differential equations using neural networks based on PyTorch, used by multiple research groups around the world, including at Harvard IACS.
Physics-Informed Neural networks for Advanced modeling
Neural network based solvers for partial differential equations and inverse problems 🌌. Implementation of physics-informed neural networks in pytorch.
Physics-informed neural network for solving fluid dynamics problems
Codebase for PINNacle: A Comprehensive Benchmark of Physics-Informed Neural Networks for Solving PDEs.
This repository containts materials for End-to-End AI for Science
Deep learning library for solving differential equations on top of PyTorch.
A remix of Arch Linux ARM for Raspberry Pi 3 B+ built for HackRF and RTL-SDR
A large-scale benchmark for machine learning methods in fluid dynamics
Generative Pre-Trained Physics-Informed Neural Networks Implementation
resources pour le cours d'introduction à la programmation des GPUs du mastère spécialisé HPC-AI
DAS: A deep adaptive sampling method for solving high-dimensional partial differential equations
PiNN2 is a easy-to-use framework for device compact modeling using physics-inspired neural networks
PINN (Physics-Informed Neural Networks) on Navier-Stokes Equations
A simple, templated script to create PINN-compatible compressed tarballs and metadata from an OS disk image
Example problems in Physics informed neural network in JAX
Downloading and converting OpenWrt image to Raspberry Pi NOOBS/PINN os format
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